Results are shown in Table REF . Different to COCO, we find that the supervised pre-trained model slightly outperforms the MIM counterpart on ADE-20K semantic segmentation. Therefore, for the combined tasks, it may be difficult to predict which pretrained model will perform better. But if the model gets larger, MIM models still have the unique advantage that MIM tasks are harder to be overfitted than supervised tasks [1]}, [2]}, which is beyond the scope of this paper.
Also, we can observe that the performance gap between supervised and MIM models on Mask2former is smaller than that of UperNet (\(-1.6\)  v.s. \(-0.6\) ). This may be due to that Mask2former decomposes the semantic segmentation task into object localization and recognition tasks, while MIM is better at object localization tasks, as shown in Figure 7 of the main paper.
